---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-test_arm-colab-CV16.0
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: hy-AM
      split: test
      args: hy-AM
    metrics:
    - name: Wer
      type: wer
      value: 0.1774802773129333
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# w2v-bert-2.0-test_arm-colab-CV16.0

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2066
- Wer: 0.1775

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1471        | 1.6   | 300  | 0.2062          | 0.2229 |
| 0.1437        | 3.2   | 600  | 0.2216          | 0.2375 |
| 0.1051        | 4.8   | 900  | 0.1969          | 0.2127 |
| 0.0594        | 6.4   | 1200 | 0.1882          | 0.1839 |
| 0.0297        | 8.0   | 1500 | 0.1951          | 0.1825 |
| 0.0115        | 9.6   | 1800 | 0.2066          | 0.1775 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.1